1. Field of the Invention
This invention relates to object detection.
2. Description of the Prior Art
Many object detection algorithms, such as human-face detection algorithms, have been proposed in the literature, including the use of so-called eigenfaces, face template matching, deformable template matching or neural network classification. None of these is perfect, and each generally has associated advantages and disadvantages. None gives an absolutely reliable indication that an image contains a face; on the contrary, they are all based upon a probabilistic assessment, based on a mathematical analysis of the image, of whether the image has at least a certain likelihood of containing a face. The algorithms generally have a threshold likelihood value that is usually set quite high to try to avoid false detections of faces.
Face detection in video material, comprising a sequence of captured images, is a more complicated than detecting a face in a still image. It is often desirable to be able to “track” a face through a sequence of images so that its movement may be determined and a corresponding so-called “face-track” may be produced. This allows, for example, a face detected in consecutive images to be linked to the same person. One way of attempting to track faces through a sequence of images is to check whether two faces in adjacent images have the same or very similar image positions. However, this approach can suffer problems because of the probabilistic nature of the face detection schemes. On the one hand, if the threshold likelihood (for a face detection to be made) is set high, there may be some images in the sequence where a face is present but is not detected by the algorithm, for example because the owner of that face turns his head to the side, or his face is partially obscured, or he scratches his nose, or one of many possible reasons. On the other hand, if the threshold likelihood value is set low, the proportion of false detections will increase and it is possible for an object which is not a face to be successfully tracked through a whole sequence of images.
Whilst processing a video sequence, a face tracking algorithm may track many detected faces and produce corresponding face-tracks. It is common for several face-tracks to actually correspond to the same face. As mentioned above, this could be due, for example, to the owner of the face turning his head to one side and then turning his head back. The face tracking algorithm may not be able to detect the face whilst it is turned to one side. This results in a face-track for the face prior to the owner turning his head to one side and a separate face-track for the same face after the owner has turned his head back. This may be done many times, resulting in two or more face-tracks for that particular face. As another example, a person may enter and leave a scene in the video sequence several times, this resulting in a corresponding number of face-tracks for the same face.